In this digital age, merchants are challenged by customers who want near instantaneous transactions across multiple channels and the need to reduce fraudulent transactions. At the same time, merchants struggle to enhance fraud detection for the rapidly growing market of digital goods.
With the prevalence of computers, mobile telephones and the Internet, buyers and sellers can now interact without seeing one another; card-not-present (CNP) transactions in which the merchant never sees the payment card are now much more common. As e-commerce becomes more popular and digital sales and delivery more common, merchants struggle to contain fraudulent payments without inconveniencing customers. A glitch during an e-commerce transaction can result in loss of the transaction for the merchant.
Unfortunately, card-not-present transactions—many of which are performed using computers, a telephone or mobile devices—are responsible for a great deal of fraud. But, increasing fraud controls and tightening up fraud detection algorithms in order to detect fraud during a transaction can result in a good transaction being denied and a good customer being turned away. Most fraud detection systems are not yet able to handle the increase in online card-not-present transactions and especially the sale and delivery of digital goods.
One reason that prior art fraud detection systems have difficulty has to do with the modeling technology that is used. In particular, card-not-present fraud has very dynamic attack patterns. This creates a challenge for the payment processing industry to devise a single modeling technology that is effective for all fraud attack patterns over an extended period of time. The current industry practice is to select one modeling technology and then to train that model which is rarely changed in response to fraud attack patterns that do change over time. A single model may not be effective for different transaction channels when a fraud attack pattern changes. Currently, it is not practical to attempt to train a model or to change models when a fraud pattern changes.
Thus, further techniques and systems are desired to improve the ability of a fraud detection system to react quickly when fraud patterns change.